Comparative Canopy Growth Dynamics in Four Row Crops and Their Relationships with Allometric and Environmental Determinants

CORE IDEAS: We measured and compared canopy height, leaf area index, and aboveground biomass and their normalized and integrative forms (net assimilation rate and leaf area ratio).Indices were modeled using heat accumulation with strong correlation and transferability.Sensitivity of biomass production was quantified, which was higher for C4 than C3 crops. Quantitative estimates of crop growth for regionally dominant cropping systems are imperative for various agricultural applications. Here, we studied growth patterns for maize, soybean, sorghum, and winter wheat grown under optimal growing conditions in South‐Central Nebraska during two growing seasons (2016–2017). The objectives were to measure and compare crop‐specific growth indicators such as canopy height (CH), leaf area index (LAI) and aboveground biomass (AGB), crop growth rate (GR), relative growth rate (RGR) and their normalized (LAI per plant[LAIpP], LAI per canopy height[LAIpCH], AGB per plant [AGBpP], and AGB per canopy height [AGBpCH]), and integrative forms (net assimilation rate [NAR] and leaf area ratio [LAR]). Crop‐specific sub‐seasonal magnitudes and patterns were found, which were uniform across years. Crop growth indices were empirically modeled using heat accumulation (NCTU), owing to strong correlation (mostly R² > 0.80) and transferability. Sensitivity of biomass production between sampling events to heat accumulated between these events was quantified, which was higher for C4 crops (2.9–3.3 g m⁻² °C⁻¹) than C3 crops (0.64–1.01 g m⁻² °C⁻¹). Empirical models for estimating relatively complex growth indices were presented using indices such as CH and LAI. Moreover, LAR and RGR were highly correlated (R = 0.86) across all crops, while NAR and RGR were moderately correlated (R = 0.43), although substantial crop‐specific variability existed among these indices. The data, comparisons and models presented are the first investigated values for these crops in Midwest under uniform conditions, and are valuable for applications in remote estimation of crop growth, crop models, crop resource use, and other related applications.